Section 1: Information about the file -

  1. Total records (RangeIndex)
Out[20]:
992035
  1. Number of Columns (Data Columns)
Out[21]:
35
  1. Column Names and Data Type
Out[22]:
Index(['District', 'Janpad', 'Gram Panchayat', 'Village',
       'Name of head of household', 'GOI ID', 'Column_1', 'Economic status',
       'Sub-category', 'Has Toilet', 'Type of toilet',
       'Is the toilet being used', 'Water source', 'Reason for not using',
       'No Toilet Seat', 'Water Seal Not Connected to Toilet Seat',
       'No/Broken Platform', 'No Water', 'Used as Animal Shelter', 'No Wall',
       'No Door', 'No Chamber', 'Pit Full', 'No/Broken Pit', 'No Cap',
       'Used as Storage', 'Used for other purpose', 'No Reason', 'State',
       'Total Household', '% Headed by Women',
       '% Headed by Person with Disability', '% Having No Toilet',
       '% Having No Water Source',
       '% Households having Toilet but no Water Source'],
      dtype='object')

Section 2: Snapshot of the data -

Districts:  51   |  Janpad:  311   |  Gram Panchayats:  13560   |  Villages:  22388   |  Surveys:  992035

Does the data contain any empty cell?

True - Yes
False - No
Out[24]:
False

Identify duplicate records

Based on the combination - District+Janpad+Gram Panchayat+Village+Name of head of household+GOI ID

Section 3: Data Error Checks

1. Total Duplicate records:

In [26]:
"""Data Error Condition 1: Number of Duplicate Records identified using Duplicate Key above"""
raw_data_dup = raw_data[raw_data["is_duplicate_record"] == True]
raw_data_dup.shape[0]
Out[26]:
7285

Deduplicating the data

Number of unique Records -

Out[27]:
984750

2. Missing Data in Mandatory Columns:

-----
Series([], Name: Column_1, dtype: int64)
Missing Data:  984750

Observations:

a. All the values in Column_1 are missing. b. There is no other missing value error.

3. Number of records where toilet is available in the household but type of toilet has either not been provided or mentioned as not available -

No of Records: 1625  ( 0.17 %)

4. Number of records where toilet is not available in the household but type of toilet has been mentioned -

No of Records: 358  ( 0.04 %)

5. Number of records where toilet is not available in the household but reason for not using it is mentioned -

No of Records: 2891  ( 0.29 %)

Section 2: Data Analysis

1. Overview

Total Households Surveyed in Reverification of Toilet:

Out[32]:
984750

Households by Economic Status

Number of households by Economic Status
-----------------------
     Total Households      %
BPL            572798  58.17
APL            411952  41.83

Observations:

  1. 58.16% of the households where toilet is not usable belong to BPL category.

Households by Social Category

Number of households in
-----------------------
ST                                  346882
GENERAL                             226422
Other                               185947
SC                                  132566
Small & Marginal Farmers             63635
Women Headed HH                      21111
Landless with homestead               5597
Headed by Person with Disability      2590
Name: Sub-category, dtype: int64
-----------------------
% of households in
-----------------------

Households by Economic Status and Social Category

Observations -

  1. There are 18.88% households in "Others" category
  2. All the General category households are BPL
  3. BPL Households has only General, SC and ST category. No landless family, or household headed by a woman or physically challenged person has been reported as BPL.

2. Status of Toilets in Households

Availability of Toilets in the Households

Number of households having toilet
-----------------------
Yes    552367
No     432383
Name: Has Toilet, dtype: int64
-----------------------
Distribution of Household with/without toilet by Economic Status:
Has Toilet Yes No
Economic status
BPL 339390 233408
APL 212977 198975
Distribution of Household with/without toilet by Social Category:
Sub-category Small & Marginal Farmers Other GENERAL ST SC Landless with homestead Women Headed HH Headed by Person with Disability
Has Toilet
Yes 34720 73269 123822 228642 75028 3251 12129 1506
No 28915 112678 102600 118240 57538 2346 8982 1084

Observations:

  1. 43% of the housheolds surveyed for unusable toilet has reported that they don't have toilet i.e. 4.36 lakh such households are there

Availabilty of Different Types of Toilet across Households

Number of households having different types toilet
--------------------------------------------------
Single Pit                   342452
Double Pit                   170610
Septic Tank                   24634
Septic Tank with Soak Pit     11343
Bio Toilet                     1703
Not Available                     0
Name: Type of toilet, dtype: int64
--------------------------------------------------

Reasons for Not Using the Toilet

Observation:

Major reasons for no usage of toilet is the superstructure defect

  1. 54% of the households having toilet are not using it due to lack of or broken Pit.
  2. 40% of the households having toilet are not using it in lack of a door.
  3. 36% of the households having toilet are not using it due to lack of or broken Platform.
  4. 34% of the households having toilet are not using it due to lack of toilet seat.
  5. 28% of the households having toilet are not using it due to lack of water.

Prominent reasons for no use of toilets across in districts, janpad and gram panchayats

In [47]:
raw_data.pivot_table(index=['District','Janpad','Gram Panchayat'], 
                     values=['Total Household', 
                            'No Toilet Seat',
                            'Water Seal Not Connected to Toilet Seat',
                            'No/Broken Platform',
                            'No Water',
                            'Used as Animal Shelter',
                            'No Wall',
                            'No Door',
                            'No Chamber',
                            'Pit Full',
                            'No/Broken Pit',
                            'No Cap',
                            'Used as Storage',
                            'Used for other purpose',
                            'No Reason'],
                     aggfunc={'Total Household':np.sum, 
                            'No Toilet Seat':np.mean,
                            'Water Seal Not Connected to Toilet Seat':np.mean,
                            'No/Broken Platform':np.mean,
                            'No Water':np.mean,
                            'Used as Animal Shelter':np.mean,
                            'No Wall':np.mean,
                            'No Door':np.mean,
                            'No Chamber':np.mean,
                            'Pit Full':np.mean,
                            'No/Broken Pit':np.mean,
                            'No Cap':np.mean,
                            'Used as Storage':np.mean,
                            'Used for other purpose':np.mean,
                            'No Reason':np.mean},
                    margins=True).apply(lambda x: round(x*100,2))
Out[47]:
No Cap No Chamber No Door No Reason No Toilet Seat No Wall No Water No/Broken Pit No/Broken Platform Pit Full Total Household Used as Animal Shelter Used as Storage Used for other purpose Water Seal Not Connected to Toilet Seat
District Janpad Gram Panchayat
AGAR MALWA AGAR AMLA AGAR 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 3300 0.00 0.00 0.00 0.00
BAJANA 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 300 0.00 0.00 0.00 0.00
BAPCHA 0.00 0.00 0.00 92.31 7.69 0.00 0.00 0.00 0.00 0.00 1300 0.00 0.00 0.00 0.00
BATAVADA 0.00 0.00 15.38 61.54 0.00 0.00 0.00 7.69 15.38 0.00 1300 0.00 0.00 0.00 0.00
BHADWA 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 2100 0.00 0.00 0.00 0.00
BHYANA 2.44 3.05 45.73 2.44 70.12 25.00 41.46 6.10 26.83 3.05 16400 0.00 5.49 0.61 1.83
BIJANAKHEDI 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 200 0.00 0.00 0.00 0.00
CHACHAKHEDI 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 700 0.00 0.00 0.00 0.00
CHAKBADABEED 0.00 0.00 10.59 3.53 2.35 0.00 96.47 14.12 0.00 0.00 8500 0.00 0.00 0.00 0.00
CHANDANGAON 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00 0.00 0.00 100 0.00 0.00 0.00 0.00
DHOTI 2.44 0.00 0.00 87.80 0.00 0.00 9.76 0.00 0.00 0.00 4100 0.00 0.00 0.00 2.44
FATHAPURMANDKI 0.00 0.00 11.11 77.78 0.00 0.00 0.00 0.00 11.11 0.00 900 0.00 0.00 0.00 0.00
GATA 0.00 0.00 20.00 80.00 0.00 0.00 0.00 0.00 0.00 0.00 500 0.00 0.00 0.00 0.00
GUNDIKALAN 0.00 0.00 0.00 84.21 5.26 0.00 0.00 5.26 15.79 5.26 1900 0.00 0.00 0.00 0.00
HADAI 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 600 0.00 0.00 0.00 0.00
HARGANKHEDI 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 600 0.00 0.00 0.00 0.00
JAMUNIYA 14.29 8.57 34.29 0.00 40.00 20.00 8.57 42.86 14.29 11.43 3500 2.86 2.86 2.86 14.29
JATPURA 10.00 14.17 5.83 15.00 72.50 0.83 57.50 15.83 73.33 2.50 12000 0.00 0.00 0.00 8.33
JHALARA 26.47 2.94 17.65 5.88 47.06 2.94 47.06 0.00 38.24 2.94 3400 0.00 0.00 0.00 2.94
KASAIDAHRIYA 0.00 0.00 6.17 19.75 13.58 0.00 35.80 4.94 13.58 0.00 8100 0.00 6.17 0.00 1.23
KHAKRI 0.00 0.00 0.00 93.75 3.12 6.25 0.00 0.00 0.00 0.00 3200 0.00 0.00 0.00 0.00
LADWAN 0.00 5.13 7.69 71.79 7.69 7.69 5.13 15.38 12.82 2.56 3900 0.00 0.00 0.00 28.21
MAHUDIYA 2.30 1.15 1.15 80.46 6.90 1.15 4.60 1.15 4.60 2.30 8700 0.00 0.00 0.00 13.79
MALWASA 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 300 0.00 0.00 0.00 0.00
MATHURAKHEDI 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 200 0.00 0.00 0.00 0.00
NANYAKHEDIAHIR 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 24900 0.00 0.00 0.00 0.00
NARWAL 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 100 0.00 0.00 0.00 0.00
NIPANIYABAJNATH 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 200 0.00 0.00 0.00 0.00
PACHORA 0.00 0.00 1.27 87.34 3.80 2.53 3.80 2.53 1.27 1.27 7900 0.00 0.00 0.00 0.00
PALKHEDI 0.00 0.00 0.00 95.00 0.00 5.00 0.00 5.00 0.00 0.00 2000 0.00 0.00 5.00 0.00
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
VIDISHA VIDISHA HANSUA 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 100 0.00 0.00 0.00 0.00
HAROOKHEDI 0.00 0.00 0.00 0.00 0.00 16.67 0.00 16.67 16.67 33.33 600 0.00 0.00 0.00 16.67
IMLIYA LASHKARPUR 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 800 0.00 0.00 0.00 0.00
KANKARKHEDI 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 200 0.00 0.00 0.00 0.00
KARAIYA HAWELI 0.00 0.00 0.00 0.00 0.00 0.00 33.33 66.67 66.67 0.00 600 0.00 0.00 0.00 33.33
KARARIYA AHAMADPUR 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 200 0.00 0.00 0.00 0.00
KHAMKHEDA KASBA 0.00 0.00 0.00 80.00 0.00 0.00 20.00 0.00 0.00 0.00 500 0.00 0.00 0.00 0.00
KHAMKHEDA LASHKARPUR 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 100 0.00 0.00 0.00 0.00
KHEJDA SULTAN 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 100 0.00 0.00 0.00 0.00
KOULINJA 0.00 0.00 20.00 20.00 60.00 0.00 20.00 0.00 0.00 0.00 500 0.00 0.00 0.00 0.00
KUAKHEDI 0.00 0.00 4.35 65.22 4.35 8.70 4.35 17.39 0.00 10.87 4600 0.00 0.00 0.00 4.35
LASHKARPUR 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 200 0.00 0.00 0.00 0.00
NATHANPUR 0.00 0.00 100.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 100 0.00 0.00 0.00 0.00
NITRRI 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 100 0.00 0.00 0.00 0.00
PATHHARI HAWELI 0.00 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 100 0.00 0.00 0.00 0.00
PIPALKHEDA KALA 0.00 9.09 0.00 90.91 9.09 0.00 0.00 0.00 0.00 9.09 1100 0.00 0.00 0.00 0.00
PIPARIYA AJIT 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 100 0.00 0.00 0.00 0.00
POUANALA 0.00 0.00 30.30 6.06 0.00 0.00 69.70 27.27 0.00 0.00 3300 0.00 9.09 0.00 0.00
PURAINIYAN 0.00 0.00 83.33 0.00 33.33 0.00 0.00 0.00 0.00 0.00 600 0.00 0.00 0.00 0.00
RUSALLI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00 0.00 100 0.00 0.00 0.00 0.00
SAHAJAKHEDI 40.00 60.00 30.00 0.00 10.00 0.00 10.00 20.00 30.00 10.00 1000 0.00 20.00 0.00 0.00
SANKALKHEDA KALAN 3.33 3.33 76.67 6.67 13.33 6.67 6.67 40.00 3.33 0.00 3000 0.00 0.00 0.00 6.67
SANKALKHEDA KHURDA 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 800 0.00 0.00 0.00 0.00
SANOTI 40.00 0.00 0.00 40.00 0.00 0.00 0.00 0.00 20.00 0.00 500 0.00 0.00 0.00 0.00
SAYAR 0.00 0.00 4.00 94.00 2.00 4.00 0.00 0.00 0.00 0.00 5000 0.00 0.00 0.00 0.00
SONTHAR 0.00 0.00 4.55 95.45 0.00 0.00 0.00 0.00 0.00 0.00 2200 0.00 0.00 0.00 0.00
SOUTHHIYA 0.00 0.00 67.35 0.00 32.65 2.04 2.04 36.73 4.08 2.04 4900 0.00 0.00 2.04 0.00
SULTANIYA 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 200 0.00 0.00 0.00 0.00
SUNPURA 10.00 0.00 50.00 0.00 20.00 30.00 0.00 0.00 90.00 0.00 1000 10.00 20.00 0.00 10.00
All 12.42 12.24 22.62 44.10 19.28 8.09 15.95 30.62 20.52 9.18 98475000 1.07 4.81 2.91 13.77

16279 rows × 15 columns

3. Status of Water Sources in the Households

Availabilty of Different Types of Water Sources across Households

Number of households having different types of water source
--------------------------------------------------
Handpump                  456468
Well                      182049
No Source of Water        176436
Piped Water Connection    147330
Data Missing               15958
Pond                        6509
Name: Water source, dtype: int64
--------------------------------------------------

Availability of Toilets and Water Source in the Households

Has Toilet                 Yes      No
Water source                          
Handpump                273968  182500
Well                    113899   68150
Piped Water Connection   73854   73476
Data Missing             11237    4721
No Source of Water       75688  100748
Pond                      3721    2788

Observation:

  1. Handpump is the major source of water in households (46% of households use handpumps)
  2. Water Supply scheme is available in only 15% of the households.
  3. 18.5% of the households rely on wells
  4. Around 87 Thousand households having toilets has no water source

Section 3: Status of toilets across State and Districts

Out[51]:
% Having No Toilet % Having No Water Source % Headed by Person with Disability % Headed by Women % Households having Toilet but no Water Source Total Household
District
AGAR MALWA 53.274 16.5501 0.105955 1.41979 7.2473 4719
ALIRAJPUR 23.2877 1.28767 0.30137 2.19178 0.821918 3650
ANUPPUR 16.1503 1.8725 0.317372 4.348 1.79315 25207
ASHOKNAGAR 22.0045 0.627983 0.175835 1.83371 0.401909 3981
BALAGHAT 47.0487 0.264727 0.130909 1.50982 0.168727 34375
BARWANI 53.1538 0.868991 0.0165207 0.545184 0.234594 30265
BETUL 41.2732 0.206981 0.229358 2.47259 0.162229 17876
BHIND 73.0364 0.119308 0.258501 3.08212 0.119308 5029
BHOPAL 51.2683 1.35763 0.178635 1.53626 0.857449 2799
BURHANPUR 45.6361 0.130265 0.0955276 1.81502 0.112896 11515
CHHATARPUR 45.038 2.86608 0.265132 2.85813 2.01766 37717
CHHINDWARA 34.064 0.520156 0.408694 2.92278 0.427271 16149
DAMOH 40.1546 0.175336 0.308166 2.5344 0.119547 37642
DATIA 73.1746 6.24579 0.269602 1.86475 3.7295 4451
DEWAS 63.8385 2.61864 0.161147 1.80485 1.83708 12411
DHAR 35.0046 0.288752 0.170626 1.82439 0.0131251 7619
DINDORI 38.099 3.64496 0.0707398 2.12964 3.06043 26859
GUNA 42.5196 0.455516 0.223013 1.40925 0.336892 21075
GWALIOR 48.3982 14.342 0.147856 1.97141 9.85707 2029
HARDA 33.8396 4.96152 0.222762 0.9113 4.21223 4938
HOSHANGABAD 38.6964 0.30636 0.316924 1.14093 0.253539 9466
INDORE 44.7296 3.20807 0 2.65811 2.29148 1091
JABALPUR 67.033 0.527537 0.175305 1.43166 0.084406 61607
JHABUA 21.4496 2.09357 0.26453 1.48137 1.29242 13231
KATNI 33.9027 8.69035 0.229928 1.90981 6.21745 21311
KHANDWA 48.8211 8.62524 0.256121 2.42561 6.6742 13275
KHARGONE 50.5703 0.515258 0.337669 2.6063 0.357679 39980
MANDLA 33.7591 1.87235 0.335073 3.02784 1.39918 49243
MANDSAUR 44.6651 3.14642 0.377922 2.93549 1.95992 11378
MORENA 67.2527 1.0708 0.351606 2.97267 0.68723 6257
NARSINGHPUR 31.8678 2.52729 0.254225 1.33094 1.92912 6687
NEEMUCH 24.5407 0.73968 0.453352 2.02816 0.441422 8382
PANNA 48.0276 0.124948 0.178497 1.47558 0.107098 16807
RAISEN 45.3374 4.62607 0.283416 2.00219 3.49241 10938
RAJGARH 47.8329 4.93543 0.338983 2.27199 3.79742 24780
RATLAM 28.1379 1.14382 0.198261 2.86716 0.793046 6557
REWA 49.4888 0.0839208 0.210565 2.13922 0.0701883 65538
SAGAR 30.9339 1.18122 0.152038 1.64903 0.701713 17101
SATNA 38.4793 0.33714 0.161241 1.44584 0.255853 75043
SEHORE 27.6676 16.023 0.269985 1.25602 11.3394 8519
SEONI 52.9359 1.25235 0.590369 3.03141 0.918176 62842
SHAHDOL 22.1994 0.22386 0.242515 2.43447 0.22386 10721
SHAJAPUR 34.569 9.22414 0.0431034 1.12069 6.25 2320
SHEOPUR 45.0135 0.539084 0.404313 2.69542 0.134771 1484
SHIVPURI 57.9999 0.0644722 0.290125 2.69351 0.0608904 27919
SIDHI 32.7771 0.141403 0.546757 2.94589 0.0848416 21216
SINGROULI 23.2956 1.01018 0.326484 2.06261 0.948723 26035
TIKAMGARH 42.4203 0.904308 0.411049 1.90727 0.739888 6082
UJJAIN 62.5226 0.0602047 0.316075 1.92655 0.0451535 6644
UMARIA 33.3645 0.508589 0.0622762 0.882246 0.0934143 19269
VIDISHA 58.4701 0.299283 0.32569 1.97174 0.286079 22721
All 43.9079 1.62051 0.263011 2.14379 1.1411 984750

Observations

  1. Jabalpur and Morena has nearly 67% of the households without toilet
  2. Sehore and Agar Malwa are the two districts with highest % of households without any source of water (nealry 16%). These two districts also have highest proportion of households having toilet but no water source.

Section 4: List of Distribution of Households

Number of duplicate records region wise

In [52]:
dup_hh=raw_data_dup.pivot_table(index=["District","Janpad","Gram Panchayat"],
                         values='Total Household',
                        aggfunc=np.sum).sort_values('Total Household', ascending=False)

create_download_link(dup_hh,filename="Toilet Not Usable - Duplicate Records.csv")

Household Toilet Status

In [53]:
hh_tnu_tlt_status = raw_data.pivot_table(index=['District','Janpad','Gram Panchayat'], 
                     values=['Total Household', 
                             '% Headed by Women', 
                             '% Headed by Person with Disability', 
                             '% Having No Toilet', 
                             '% Having No Water Source',
                            '% Households having Toilet but no Water Source'],
                     aggfunc={'Total Household':np.sum, 
                              '% Headed by Women':np.mean, 
                              '% Headed by Person with Disability':np.mean, 
                              '% Having No Toilet':np.mean, 
                              '% Having No Water Source':np.mean,
                             '% Households having Toilet but no Water Source':np.mean},
                    margins=True)

create_download_link(hh_tnu_tlt_status,filename="Toilet Not Usable - Household Toilet Status.csv")

Households by Socio Economic Condition

In [54]:
hh_by_sco_eco=raw_data.pivot_table(index=["District","Janpad", "Gram Panchayat"], 
                        columns=["Economic status", "Sub-category"],
                        values=['Total Household'],
                        aggfunc={'Total Household':np.sum}, 
                        margins=True)

create_download_link(hh_by_sco_eco,filename="Toilet Not Usable - Households by Socio Economic Condition.csv")

List of Households Not Having Toilet

In [55]:
hh_no_tlt=raw_data[raw_data['Has Toilet']!="हाँ"].pivot_table(index=["District",
                                                           "Janpad",
                                                           "Gram Panchayat",
                                                           "Village"],
                     values='Total Household',
                     aggfunc=np.sum).sort_values('Total Household', ascending=False)

create_download_link(hh_no_tlt,filename="Toilet Not Usable - Households not having toilet.csv")

Number of records where toilet is available in the household but type of toilet has either not been provided or mentioned as not available

In [56]:
raw_data_tlt_nttype.pivot_table(index=["District","Janpad","Gram Panchayat"],
                         values='Total Household',
                        aggfunc=np.sum).sort_values('Total Household', ascending=False)

create_download_link(raw_data_tlt_nttype, filename="Toilet Not Usable Error - Type of toilet not provided.csv")

Number of records where toilet is not available in the household but type of toilet has been mentioned

In [57]:
raw_data_ntlt_ttype_pv=raw_data_ntlt_ttype.pivot_table(index=["District","Janpad","Gram Panchayat"],
                         values='Total Household',
                        aggfunc=np.sum).sort_values('Total Household', ascending=False)

create_download_link(raw_data_ntlt_ttype_pv,filename="Toilet Not Usable Error - No Toilet but Type Mentioned.csv")

Number of records where toilet is not available in the household but reason for not using it is mentioned

In [58]:
raw_data_ntlt_nrsn_pv=raw_data_ntlt_nrsn.pivot_table(index=["District","Janpad","Gram Panchayat"],
                         values='Total Household',
                        aggfunc=np.sum).sort_values('Total Household', ascending=False)

create_download_link(raw_data_ntlt_nrsn_pv,filename="Toilet Not Usable Error - Toilet not used but no reason.csv")